International Journal of Advanced Mechatronic Systems
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International Journal of Advanced Mechatronic Systems (2 papers in press)
Scratch detection system of the inner surface of super long gas cylinder based on VGG-16 neural networks by Baocheng He, Linguang Li, Shijie Ren, Dianwei Qian Abstract: Aiming at the problems of difficulty and low detection accuracy in manual detection of scratches on the inner surface of ultra-long energy storage gas cylinders in the aerospace industry, a detection system for scratches on the inner surface of ultra-long gas cylinders based on VGG-16 neural network is designed. In this paper, VGG-16 recognition model is innovatively proposed to apply to the application of scratch detection. Compared with the ordinary detection model to detect the required target from the complex background, this article first processes the image acquired by the image acquisition module into a single binary image with a scratched area and a non-scratched area. The VGG-16 recognition model learns the characteristics of the scratches, so as to recognise the scratches under the ordinary background, and achieve the purpose of scratch detection. The results show that the accuracy rate of the scratch detection on the inner surface of gas cylinders reaches 98.5%, which greatly improves the accuracy rate of the scratch detection compared with the previous manual detection methods. Keywords: scratch test; image processing; neural network; linear array camera. DOI: 10.1504/IJAMECHS.2022.10044450
SVM based fault detection for double layered tank system by considering ChangeFinder's characteristics by Yosuke Furukawa, Mingcong Deng Abstract: In this paper, a fault detection scheme for a tank system using support vector machine (SVM) combined with ChangeFinder, both are machine learning methods, is studied. SVM can detect faults on a nonlinear system, but it can be late because SVM cannot recognise the nature of the time series. Combination with ChangeFinder enables SVM to recognise the nature of time series, and early detection using SVM becomes possible. Simulations and experiments assuming the temperature sensor fault case for the temperature control system of the tank system have been done and 5 s reduction of detection time was confirmed. In addition to the situation of the tank system, the sine wave input showed the effectiveness of the proposed method for general input. In addition, the superiority of the proposed method over ChangeFinder in an experimental environment was confirmed. Keywords: nonlinear control; fault detection; fault tolerance; ChangeFinder; support vector machine; SVM. DOI: 10.1504/IJAMECHS.2022.10044457